Abstract
Advancements in printing and scanning technology enable fraudsters to tamper with identity documents such as identity cards, drivers’ licenses, admit cards, examination hall tickets and academic transcripts. Several security features are incorporated in important identity documents to counter forgeries and verify genuineness, but these features are often lost in printed versions of the documents. At this time, a satisfactory method is not available for authenticating a person’s facial image (photograph) in a printed version of a document. Typically, an official is required to check the person’s image against an image stored in an online verification database, which renders the problem even more challenging.
This chapter presents an automated, low-cost and efficient method for addressing the problem. The method employs printed quick response codes corresponding to low-resolution facial images to authenticate the original and printed versions of identity documents.
Chapter PDF
Similar content being viewed by others
References
S. Aygun and M. Akcay, Securing biometric face images via steganography for QR code, Proceedings of the Eighth International Conference on Information Security and Cryptology, pp. 128–133, 2015.
S. Chhabra, G. Gupta, M. Gupta and G. Gupta, Detecting fraudulent bank checks, in Advances in Digital Forensics XIII, G. Peterson and S. Shenoi (Eds.), Springer, Cham, Switzerland, pp. 245–266, 2017.
A. Espejel-Trujillo, I. Castillo-Camacho, M. Nakano-Miyatake and H. Perez-Meana, Identity document authentication based on VSS and QR codes, Procedia Technology, vol. 3, pp. 241–250, 2012.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville and Y. Bengio, Generative adversarial nets, Proceedings of the Twenty-Seventh Annual Conference on Neural Information Processing Systems, pp. 2672–2680, 2014.
R. Gross, I. Matthews, J. Cohn, T. Kanade and S. Baker, Multi-PIE, Image and Vision Computing, vol. 28(5), pp. 807–813, 2010.
G. Gupta, S. Saha, S. Chakraborty and C. Mazumdar, Document frauds: Identification and linking fake documents to scanners and printers, Proceedings of the International Conference on Computing: Theory and Applications, pp. 497–501, 2007.
P. Isola, J. Zhu, T. Zhou and A. Efros, Image-to-Image Translation with Conditional Adversarial Networks, arXiv:1611.07004 (arxiv.org/abs/1611.07004), 2018.
J. Nayak, S. Singh, S. Chhabra, G. Gupta, M. Gupta and G. Gupta, Detecting data leakage from hard copy documents, in Advances in Digital Forensics XIV, G. Peterson and S. Shenoi (Eds.), Springer, Cham, Switzerland, pp. 111–124, 2018.
O. Parkhi, A. Vedaldi and A. Zisserman, Deep face recognition, Proceedings of the British Machine Vision Conference, pp. 41.1–41.12, 2015.
A. Rai, I have been asked to shut my mouth, but work will go on – An interview with the whistleblower who exposed Madhya Pradesh Vyapam scam, The News Minute, February 25, 2015.
N. Raval, A. Machanavajjhala and L. Cox, Protecting visual secrets using adversarial nets, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1329–1332, 2017.
S. Sarkar, R. Verma and G. Gupta, Detecting counterfeit currency and identifying its source, in Advances in Digital Forensics IX, G. Peterson and S. Shenoi (Eds.), Springer, Berlin Heidelberg, Germany, pp. 367–384, 2013.
V. Seenivasagam and R. Velumani, A QR code based zero-watermarking scheme for authentication of medical images in teleradiology cloud, Computational and Mathematical Methods in Medicine, article no. 516465, 2013.
I. Tkachenko, W. Puech, C. Destruel, O. Strauss, J. Gaudin and C. Guichard, Two-level QR code for private message sharing and document authentication, IEEE Transactions on Information Forensics and Security, vol. 11(3), pp. 571–583, 2016.
P. Tyre, How sophisticated test scams from China are making their way into the U.S., The Atlantic, March 21, 2016.
M. Warasart and P. Kuacharoen, Paper-based document authentication using digital signature and QR code, Proceedings of the Fourth International Conference on Computer Engineering and Technology, pp. 94–98, 2012.
X. Wu, R. He, Z. Sun and T. Tan, A light CNN for deep face representation with noisy labels, IEEE Transactions on Information Forensics and Security, vol. 13(11), pp. 2884–2896, 2018.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 IFIP International Federation for Information Processing
About this paper
Cite this paper
Singh, S., Chhabra, S., Gupta, G., Gupta, M., Gupta, G. (2019). Quick Response Encoding of Human Facial Images for Identity Fraud Detection. In: Peterson, G., Shenoi, S. (eds) Advances in Digital Forensics XV. DigitalForensics 2019. IFIP Advances in Information and Communication Technology, vol 569. Springer, Cham. https://doi.org/10.1007/978-3-030-28752-8_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-28752-8_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-28751-1
Online ISBN: 978-3-030-28752-8
eBook Packages: Computer ScienceComputer Science (R0)